The disclosure relates, in general, to the design and selection of synthetic peptides for interrogating biomarkers and, more particularly, to a system and method for identifying and implementing a synthetic classifier including one or more variant peptides for diagnostic and predictive applications.
Biomarkers are naturally occurring, biological elements (e.g., nucleic acids, proteins, small molecules, and the like) that are generally detected in the blood, urine, or another fluid of a subject. Such biomarkers may form the basis of a diagnostic or prognostic classifier. One example of a biomarker includes microRNAs (miRNAs), which can have unique profiles in a subject that can be indicative of the presence or absence of a given disease and can further be predictive of disease progression. In the context of non-small cell lung cancer (NSCLC), Gasparini et al. developed a diagnostic classifier that demonstrated the expression signatures of various miRNAs could be used to classify NSCLCs as ALK-translocated, mutant EGFR, or mutant KRAS versus mutation-free (Gasparini et al. 2015, microRNA classifiers are powerful diagnostic/prognostic tools in ALK-, EGFR-, and KRAS-driven lung cancers. PNAS, vol. 112, no. 48, pp. 14924-14929). Gasparini et al. further identified a prognostic miRNA-based classifier to predict overall survival.
One potential drawback of the approach taken by Gasparini et al. and others, is that a given classifier is limited to those biomarkers derived from a given subject that are naturally occurring (wild-type or mutant). Moreover, development of such biomarker-based classifiers can be time-consuming both in terms of the length of time it takes to identify and validate the classifier as well as the extent of the labor required to process samples, design experiments, analyze date, and the like.
Accordingly, there is a need for improved processes and systems for the development of new classifiers for both diagnostic and prognostic applications.